Recently, as a technique for preventing a vehicle such as an automobile or a predetermined object such as a street light on a shoulder from colliding with an object relatively approaching the predetermined object, there has been demand for development of a technique for predicting time to collision (TTC) between the predetermined object and the object. This type of technique includes, for example, a technique for predicting the time to collision between the vehicle and the object based on images picked up by a surveillance camera such as a camera mounted on the vehicle or a camera fixedly mounted a street light. When images picked up by a vehicle-mounted camera are used, since digitized image data is used unlike when a radar is used, complex determinations on an approach angle of the object and the like can be made.
Conventionally, this type of techniques include, for example, a technique for predicting the time to collision (TTC) based on scaling-up factors of an object in a source image and a technique for predicting the time to collision (TTC) based on a position of the ground and a position of the object in the source image.
On the other hand, image processing techniques for detecting objects in source images have advanced remarkably in recent years, aiming at reducing a time required for detection while improving detection accuracy. This type of object detection techniques includes, for example, a technique which uses a HOG (Histogram of Oriented Gradients) feature value.
An object detection process (hereinafter referred to as a HOG process) which uses a HOG feature value can detect an object by scaling up and down a single source image in a predetermined period of time, thereby preparing plural images (hereinafter referred to as an image pyramid), and scanning each of the plural images using a frame of a same size.
The plural images making up an image pyramid differ from one another in a scaling factor and the object is shown in different sizes in the plural images. Consequently, with a technique for detecting an object using an image pyramid, a distance to the object can be predicted approximately from the scaling factor of a component image in which a scanned frame and the object almost coincide in size out of component images of the image pyramid.
However, the scaling factor of each component image of the image pyramid is a discrete value, making it difficult for the technique for detecting an object using an image pyramid to accurately predict the time to collision (TTC). Also, if the technique for predicting TTC based on scaling-up factors of an object in a source image is applied to the technique for detecting an object using an image pyramid, a scaling-up factor of the object takes an outlier, making it difficult to accurately predict TTC. Also, when the position of the ground is used as with the technique for predicting TTC based on a position of the ground and a position of the object in the source image, error will become very large in distant locations.